According to Google Gemini (@GeminiApp) on X, the new Deep Think tool is now available to all Gemini Ultra subscribers on gemini.google and the mobile app, enabling multi-step reasoning for complex tasks like generating production-ready SVG animations and structured code; the post details access steps and invites users to test prompts and share outputs. As reported by the Google Gemini account, Deep Think is accessed via the Tools menu and is positioned for power users who need longer-chain reasoning, which signals a push into premium AI assistant capabilities for developers and designers. According to the original post, the suggested prompt focuses on complex SVG animation valued by engineers with a Unity background, indicating practical applications in rapid prototyping, design systems, and interactive visualization workflows.

According to Google Gemini on X (@GeminiApp), the showcased demo interface is built entirely in SVG and generated using Gemini’s Deep Think mode, highlighting AI-assisted code generation for complex vector animations (source: Google Gemini post, Apr 22, 2026). As reported by the Google Gemini video post, Deep Think guides stepwise reasoning to output SVG markup and animation logic, enabling layered timelines, easing, and stateful interactions without external canvas libraries. According to Google Gemini, this capability allows developers to quickly prototype motion-rich UIs, export clean SVG, and iterate via prompts, opening opportunities for teams to accelerate design-to-code workflows, generate reusable animation snippets, and reduce front-end engineering time for marketing pages, dashboards, and data visualizations. As stated by Google Gemini, practical business impact includes lowering production costs for interactive product tours and onboarding flows, faster A/B testing of motion variants, and easier localization by keeping text as SVG elements while preserving animation structure.

According to @OriolVinyalsML, a prompt requesting an SVG of a pelican riding a car in France with a cat beside it and the Eiffel Tower in the background showcases growing demand for multimodal generative models that output structured vector graphics. As reported by Twitter/X, such scene-rich prompts underscore business opportunities for design automation, marketing creatives, and lightweight web graphics where SVG output is preferred for scalability and fast rendering. According to industry analyses on generative design, models that translate natural language to SVG can reduce creative iteration time and enable programmatic A/B testing for ads and games, while also requiring robust spatial reasoning and layered object control. As noted by DeepMind publications, advancing text-to-image and text-to-graphics alignment is central to improving compositional accuracy, which is critical for enterprise workflows in ecommerce banners, social posts, and dynamic personalization.

According to OriolVinyalsML, Google DeepMind’s Gemini 3.1 Pro has landed with strong across-the-board performance and notable real-world improvements such as far better SVG generation and handling. As reported by Oriol Vinyals on X, these upgrades go beyond standard SOTA evals, signaling practical gains in multimodal reasoning that impact UI prototyping, vector graphics coding, and web design pipelines. According to Google’s Gemini team post shared by Vinyals, better SVG fidelity implies stronger tool-use, structured output control, and code synthesis, which can reduce iteration cycles for frontend teams and design systems. For businesses, as noted by Vinyals, these capabilities suggest faster design-to-code handoffs, improved spec adherence in generated assets, and more reliable automation in documentation and component libraries.